写sql写习惯了，学R的时候也想用到类似sql里面的merge功能，看了r帮助文档后进行了一些总结
以r文档里面的例子来介绍，假如有这样两个数据框authors和books
authors <- data.frame(
surname = I(c("Tukey", "Venables", "Tierney", "Ripley", "McNeil")),
nationality = c("US", "Australia", "US", "UK", "Australia"),
deceased = c("yes", rep("no", 4)))
books <- data.frame(
name = I(c("Tukey", "Venables", "Tierney",
"Ripley", "Ripley", "McNeil", "R Core")),
title = c("Exploratory Data Analysis",
"Modern Applied Statistics ...",
"LISP-STAT",
"Spatial Statistics", "Stochastic Simulation",
"Interactive Data Analysis",
"An Introduction to R"),
other.author = c(NA, "Ripley", NA, NA, NA, NA,
"Venables & Smith"))
如果要实现类似sql里面的inner join 功能，则用代码
m1 <- merge(authors, books, by.x = "surname", by.y = "name")
如果要实现left join功能则用代码
m1 <- merge(authors, books, by.x = "surname", by.y = "name",all.x=TRUE)
right join功能代码
m1 <- merge(authors, books, by.x = "surname", by.y = "name",all.y=TRUE)
all join功能代码
m1 <- merge(authors, books, by.x = "surname", by.y = "name",all=TRUE)
关于单变量匹配的总结就是这些，但对于多变量匹配呢，例如下面两个表，需要对k1,k2两个变量都相等的情况下匹配
x <- data.frame(k1 = c(NA,NA,3,4,5), k2 = c(1,NA,NA,4,5), data = 1:5)
y <- data.frame(k1 = c(NA,2,NA,4,5), k2 = c(NA,NA,3,4,5), data = 1:5)
匹配代码如下merge(x, y, by = c("k1","k2")) #inner join
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